Zero-Shot Image Classification
Transformers
Safetensors
clip
vision
multimodal
image-text
compressed
hxq
helix-substrate
vector-quantization
helixcode
Eval Results (legacy)
8-bit precision
Instructions to use EchoLabs33/clip-vit-large-patch14-hxq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EchoLabs33/clip-vit-large-patch14-hxq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="EchoLabs33/clip-vit-large-patch14-hxq") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("EchoLabs33/clip-vit-large-patch14-hxq") model = AutoModelForZeroShotImageClassification.from_pretrained("EchoLabs33/clip-vit-large-patch14-hxq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Ctrl+K